{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Python 控制语句"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "控制语句（statements）是一个程序能够运行的基本保障，它通过与表达式（expressions）结合构成了Python程序的基本结构。Python 所含有的基本控制语句总结如下："
   ]
  },
  {
   "attachments": {
    "1530268055%281%29.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![1530268055%281%29.png](attachment:1530268055%281%29.png)"
   ]
  },
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![1530268101%281%29.png](attachment:1530268101%281%29.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Python常用控制语句，图片来源：Mark Lutz, Learning Python 5th Edition, p.p.320 - 321"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**注意：其中条件语句和控制语句的括号是非必须的。即可以是if(x > y):或if x > y:。以下是一个循环语句的使用示例。**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Input a nmber please! Number: 5\n",
      "25\n",
      "Input a nmber please! Number: 5.5\n",
      "30.25\n",
      "Input a nmber please! Number: hi\n",
      "Not number! Try again!\n",
      "Input a nmber please! Number: stop\n",
      "STOP\n"
     ]
    }
   ],
   "source": [
    "while True:\n",
    "    msg = input(\"Input a nmber please! Number: \")\n",
    "    \n",
    "    try:\n",
    "        if msg == 'stop' or msg == '停止':\n",
    "            print(msg.upper())\n",
    "            break\n",
    "        digits = eval(msg)\n",
    "    except:\n",
    "        print('Not number! Try again!')\n",
    "    else:\n",
    "        print(digits ** 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Python中允许控制语句中出现表达式（expressions），但是不允许控制语句被作为表达式使用。比如在while循环中不允许使用单等号。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 赋值语句"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 赋值语句的不同形态"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Python 3 一共支持7种不同形态的赋值语句，Python中赋值语句的形态是相当丰富的。"
   ]
  },
  {
   "attachments": {
    "1530269679%281%29.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![1530269679%281%29.png](attachment:1530269679%281%29.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Python赋值语句不同形态总结，图片来源：Mark Lutz, Learning Python 5th Edition, p.p.340"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "其中第三、四种序列型赋值以及第五种解包型赋值（unpacked assignments）示例如下："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "yum YUM\n"
     ]
    }
   ],
   "source": [
    "spam = 'joke'\n",
    "ham = 'joe'\n",
    "[spam, ham] = ['yum', 'YUM']\n",
    "print(spam, ham)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "s p a m\n"
     ]
    }
   ],
   "source": [
    "a, b, c, d = 'spam'\n",
    "print(a, b, c, d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "s ['p', 'a', 'm']\n",
      "p a m\n"
     ]
    }
   ],
   "source": [
    "# The \"first-rest pattern\"\n",
    "a, *b = 'spam'\n",
    "print(a, b)\n",
    "print(*b)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "使用元组（或列表）对一些列变量同时赋值时，需要保证等号左右两边变量和对象数量一致，或在使用解包符号（“ * ”）的情况下左边变量数量可以小于右边对象数量。然而，“数量一致可以有多种形式”。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "o d ay\n"
     ]
    }
   ],
   "source": [
    "string = 'Today'\n",
    "a, b, c = string[1], string[2], string[3:]\n",
    "print(a, b, c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "o d ay\n"
     ]
    }
   ],
   "source": [
    "string = 'Today'\n",
    "a, b, c = *string[1:3], string[3:]\n",
    "print(a, b, c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "o d ay\n"
     ]
    }
   ],
   "source": [
    "string = 'Today'\n",
    "a, b, c = list(string[1:3]) + [string[3:]]\n",
    "print(a, b, c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "o d ay\n"
     ]
    }
   ],
   "source": [
    "string = 'Today'\n",
    "(a, b), c = string[1:3], string[3:]\n",
    "print(a, b, c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "o d ay\n"
     ]
    }
   ],
   "source": [
    "string = 'Today'\n",
    "((a, b), c) = string[1:3], string[3:]\n",
    "print(a, b, c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 [1, 2, 3] 4\n"
     ]
    }
   ],
   "source": [
    "# The \"first-rest-last\" pattern\n",
    "a, *b, c = range(5)\n",
    "print(a, b, c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "t ['o', 'b', 'e', 'o', 'r', 'n', 'o', 't', 't', 'o', 'b', 'e']\n",
      "o ['b', 'e', 'o', 'r', 'n', 'o', 't', 't', 'o', 'b', 'e']\n",
      "b ['e', 'o', 'r', 'n', 'o', 't', 't', 'o', 'b', 'e']\n",
      "e ['o', 'r', 'n', 'o', 't', 't', 'o', 'b', 'e']\n",
      "o ['r', 'n', 'o', 't', 't', 'o', 'b', 'e']\n",
      "r ['n', 'o', 't', 't', 'o', 'b', 'e']\n",
      "n ['o', 't', 't', 'o', 'b', 'e']\n",
      "o ['t', 't', 'o', 'b', 'e']\n",
      "t ['t', 'o', 'b', 'e']\n",
      "t ['o', 'b', 'e']\n",
      "o ['b', 'e']\n",
      "b ['e']\n",
      "e []\n"
     ]
    }
   ],
   "source": [
    "list1 = list(\"tobeornottobe\")\n",
    "\n",
    "while list1:\n",
    "    char, *list1 = list1\n",
    "    # This is same as:\n",
    "    # char, list1 = list1[0], list1[1:]\n",
    "    # which ensures that the list shrinks.\n",
    "    print(char, list1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 多变量赋同值"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "多变量赋同值时，若赋值对象为不可变对象（immutable object），则一个变量中对象的改变不影响其他变量。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "welcome goodday goodday\n"
     ]
    }
   ],
   "source": [
    "a = b = c = 'goodday'\n",
    "a = 'welcome'\n",
    "print(a, b, c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100\n"
     ]
    }
   ],
   "source": [
    "a = b = 100\n",
    "a += 1\n",
    "print(b)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "然而对于可变对象来说，该赋值方式可能就会产生第一章“共享内存”部分所说问题。因此，如果需要用变量表示可变对象时，推荐将每个变量分别赋值。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[666]\n"
     ]
    }
   ],
   "source": [
    "lst1 = lst2 = []\n",
    "lst1.append(666)\n",
    "print(lst2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[]\n"
     ]
    }
   ],
   "source": [
    "lst1 = []; lst2 = []\n",
    "# Better way to write this:\n",
    "# lst1, lst2 = [], []\n",
    "lst1.append(666)\n",
    "print(lst2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "但是，对于两个多变量赋予相同列表值进行‘+’或‘* ’操作除外，因为这两个操作创建了新对象。而“ * = ”则和“ += ”与这个情况相比又不一样了，因为它们只是直接改变对应地址的值。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['g', 'b', '2', '3', '1', '2'] ['g', 'b']\n"
     ]
    }
   ],
   "source": [
    "L1 = L2 = ['g','b']\n",
    "L1 = L1 + list('2312')\n",
    "print(L1, L2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['g', 'b', '2', '3', '1', '2'] ['g', 'b', '2', '3', '1', '2']\n"
     ]
    }
   ],
   "source": [
    "L1 = L2 = ['g','b']\n",
    "L1 += list('2312')\n",
    "print(L1, L2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['g', 'b', 'g', 'b', 'g', 'b', 'g', 'b'] ['g', 'b']\n"
     ]
    }
   ],
   "source": [
    "L1 = L2 = ['g','b']\n",
    "L1 = L1 * 4\n",
    "print(L1, L2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['g', 'b', 'g', 'b', 'g', 'b', 'g', 'b'] ['g', 'b', 'g', 'b', 'g', 'b', 'g', 'b']\n"
     ]
    }
   ],
   "source": [
    "L1 = L2 = ['g','b']\n",
    "L1 *= 4\n",
    "print(L1, L2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 增强型赋值"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "与许多其他编程语言一样，Python也支持一定程度的增强型赋值。"
   ]
  },
  {
   "attachments": {
    "1530329853%281%29.png": {
     "image/png": 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VazJ4DiO2tCnfdxMLNmso7W6zRtSAm9lmzuDgJJysrXo8ddZ+GyXA2lAoiNNXIiwb7sHxxEqN2UH9qDUvPpFWa1byAunZU8x5PfD2R7D4fB3JMAskoThS8gxLSIEPnL6ES4e2OVCSMU/9NmswbW2zBk3CNbHNnMBBAWZO8CCKfi7APNvcrNXKZQRY1D+5YxW9ahHq7BLiQwF0KV3wOp1RZb9BtL+T3TPPAtblwepk179E5EAPgm8NIuh9BQeiN/CnsR3oHJvFL6KzKKU28QQRTpr9DhkwuxQoXmdKMqap02aVkf5VYjMPlHCSvWuGdraZkQC3uc0cwEEBlisfhKHNZ5lbyTAUgwYWL7uyPXYn6lBIwWDsgYMZlTYj3QlF8dUQ/eXEyo7f4Fb6KR5NHWNDty4oXZ3ojSzApkow+5qbOBPwOliSMUO9NivnSwcRT9k4pGozm+VpUAbMaUqbOYNjAqxOBHRj8LPr+JwX3sWwuRZrNzAD5ojr1JIh2kB2Rdaco5j7/LD5pW+ZBUR6u9Ef/UYNFtlV/C3cD69nB8ZmV9kBOzJgjnod89mMA9Rrs6rYkc1x2tVmjcqAOU1oM4dwSIBlxiaWl2zJlQa11lrLCLBdOC7AWu1Z2kGuSTaVnWwlEVa68GbsobSftK+nw+YZ5GbrGBZs5hjtajMjAbYLEuCGoi0G75n4AmJ+U64Lri0LbjMBlllJvh4uRdRMdqIt39n7MW7+mMaPf7uIA/4g9r3xKjzbmH027FKjJusYVmzmGO1qMxLgRuCAAMsGL1j58EJdu1pzFtxAHBVgLSspnJHOrk7jiM9rIjvJYis1gw/3+sVwz/vqP2HixhJWZ0/C730PUz/atYi9mTqGVZs5RbvajAS4ETg4CUcQROvSSAF+eSEBJgjCBCTAjYAEmCAIE5AANwISYIIgCJcgASYIgnAJEmCCIAiXIAEmCIJwCRJggiAIlyABJgiCcAkSYIIgCJcgASYIgnAJEmCCIAiXIAEmCIJwCRJggiAIlyABJgiCcAkSYIIgCJdoIQGWO2LYthEn8dKT/QHTR3Zg58RtbMhDBOEkDgjwC6wmPoDfo99yWu6QXLBLRjme4sH/JjC//hyr0++j8/A0VrNrWLx2Ew9r2tTTALk/VuDMTfYtGnKb7AZvc6Nt05Tfwy0rd64t3L2gMtk699erxBqS4T3wBM7ilm57/uyjOIY6utAfmWcWcgd7bJYnuzKFQx07MTb7szxSLy+PzRzDxb7pJM5kwHIPuI6hOB7xFk/fwFiBU1Qgcw+x/dvQ/ebHmLlyAttP/x4z4V+jW3kX8e/EDnMW0LZLOoypFbkZSuZrRAcUB7a4Kd6q6WfMju2EZ8dHSOo6cUVEBteDncEd8Nm2pbe2jU0Ah6aWZQfdwP3oW3JTVbu2O6oHG2yWYxNLsWF46/psMS+LzZzEzb7pHA6VIGQWLCLXmhrFTGW/kq3HSH46ij7FCw+L3H3HLyH5oz2DRjUL6sJA9GtkWNdRM0p9tt441OxEERnShsiWastKsksxDPoO4w8Lf2LXed2+e9Y2/RyI4n6G/S2ykU6bd1yuD6s2yyG29u/KbxRrlXa12db3mDnxawxFvsR6yQf43oTXcKL/XUS+Wqu9DargZt90CudqwJtfYKLHB//7Z3Fqd1dtjtlAAQae4NaZoLpr7MZjsYOs17ZsshrruDPRB4//PYRP7a0xK+Gf3Qv/2A2k5RDYd4SXZ+TblmDOfussAp4+TNxZk8FzGLElW6TKIlZsppHF5p3z6PG+heh9u/yoTW2WXcN87BiC3ldwwECEs+t3ETuyG97utxsgwm72TWdwcBJO1m88HpO1X0lJCeK/Mf3hG1CUtxF7YEfn0YaPQZy+EmER19mdmtU6IbNJrTU5UcbJZ72Z+1EMdNgoKKJspCBw+hIuHdrWVMO+um2WQ+3YtnfmtrXZBlIzp7HL68feD2eQ2ir6tMiSX4dXeQMfJr63cWdjd/umEzgowMycD6Lo5wLMI9qmWTM+xcNbt3BfTsJ1jSSQ1k/Cie3kuWMVvZQwkmY9IbuE+FAAXUqXTTXBGsh+g2h/J7tnnjmZ3RKfZXDzEQz0/w7z2kQkHwK/8yZO3HjM3lW3+S6xSU3b7suA2aVA8TbZsK8um+lIJzDi09drOXKVTYnNPDC/XXob24yL8I2P8WZ3F3aduGYgwsu4wedmvK/jxIwmwjb4oZt90wEcFGC58kE4Zz2RrJHL0LTho4LB2IMa78sK2ox0J8vofc03ufD0Js4EvE027LNqsy2sTB1Gh28UiTQv1tpMW9pMQxvFdmMwusj+KkKIpZ+NcIcQvWdX5dutvukMjgmwOhHAGu6z6/icT1b0R7BodRkZx44MmCOuU0uGaAPZFVnXimLucyYKti2vsSMD5qjXMZ8BOoBVm2UfIDbYXbS8iWNHBsxpQ5sJXMqAOW70TYdwSIDlkhKxJGerAWtXbcDxRtbqW9IOct1jc2XBzSYm1m0mauXeeofhZmg/m+VrwK8gdP7P5WvA3UM4f/OR/c9NAmwNbTF4bsmPXBdsWxZsB043ssxK8vVwGaSaapF5k4mJZZvJ1QC9zO8aUH1QaTeb5cXXaBVEXnwbsQpCQgJsBW1BtX7lwwusTh+Dr5myYEcbWctKCmeks6vTOOLzNlEW3ExiYoPNRPbnb3Atsc1s5uI64BwkwATR6mSQnj0Jv/6XVQThMiTAxEuC+sOVTtt+rEIQ1iEBJgiCcAkSYIIgCJcgASYIgnAJEmCCIAiXIAEmCIJwCRJggiAIlyABJgiCcAkSYIIgCJcgASYIgnAJEmCCIAiXIAEmCIJwCRJggiAIlyABJgiCcAkSYIIgCJdofgHW/uO+x4vuA59i8Rn/X4JbWJn+F/S88wek6F8LErUi/sH3QcRTZf4vcLX3X2rUzRQ6d57HnQ2Dzpf9AdNHdmDnxG1syENEeRwQYNZgiQ/g9+i36ZY7JBfskmFEFhvJMLbx7VR++RIXdr+GsdlVubPBa9Z305D7YxVu0Ch3fm3w1kDaNk07wn+V352VO9cW7l7QdLSszdi5D2bxx/k1sSPE4c73Mb36HOuLs/jzQ361au9boJ38LLuMqUPb4B+7gbQ8pCe7MoVDHTtZP/1ZHqkTF23mJM5kwHIPuI6hOB7xFk/fwFiBU5RDvxU93+plO0Lxv+NedAidg5/inuX95LTtknS7JGS+RnRAcWBboOKtmn7G7NhOeHZ8hOTThn6xRVrVZht4EHsbSvevEZ75PU5vP4ErM3yX31eYED1kQlTtfSu0j59ll2IY9OqTKT2bWIoNw2uLD7tpM+dwqAQhs2ARudbUKFY1++Vk8cvsv6Fz928x/8ttTPTswb//z2UW0V8v4wC1o0bsLgxEv0aGfZ+6Y3M5B7MXNTtR0B+Zx8ajOIY66s1++caJZ9A/NImv1l/IYzoq7utVO61rsw38mLyE431+eDxeKH2j+DT5WLd3W7X366c9/OwZE8V9ug0+i8gsINLbld981yJu2swpnKsBb37BBNQH//tncWp3l4nsV/JsHtEDr7AO0YldJz7DldN7bI6AT3DrTFB1qo3HYgdZ72AMS7ZdvxJyl17/ewif2ltnVuLGrrWtajP3BLj1/Ywh+nC3FMRisti8cx493rcQvW9X9ddNmzmDg5Nwsn7j8Zio/RqRxfN7n2LQZ3f9h11X7BwbxOkrERZxnd2pOSsyEmaTempy2TUsxv+1rPhm1xcQP76nAVuGt6LNDEoM07/BXmUb9sfuMUGp9r5VWtjPBCwDvXUWAe8wYktG+a0qlvYKpLs2cwIHBZiZ80EU/VyAyw1hKpFdYRHwtdLMWcxYc8cqeilhJM2mLtklxIcC6FK6bKpf1UD2G0T7O9k992HiTg3bbjPx/SryNrq9r+PEzPclWVp2/UtE2MjBu+s0ZlLFGYm6dXqJzWrZ+rvlbMY688M5XL+fVifZukaRSG8VTsJVfP9ltJmen5AY2Y6OQ1NYMbrtdAIjvgAOTS3rBLLFbeYADgqwXPnQpUDx1hrJWPRlw5vdgaP47Np/4sguP5Rd7+NT27I6Gd09CgZjD2y6phlYpxcz0p1QFF8NpZUX+Mfsabzq6TAu5WR/xOyJXczRG1kvazWb6XBtGVrr2kytx27HSOIneUTPFlamDqPDx4OW9bFCIW7ZzBkcE2B1IqAbg59dx+eHAvD2R7BodhXD1j3EDryK/t/9Fz7euQsnZr/Ht7GD8PHhDhtaWc6AOaLT1RCZ7UBk9byuFcXc58yBa1lek8uAd+NI7K5B+UFmwMFjiPGlVfK4ig2ZCafVbGaJl9lmcnVD4CxuGWWg2QeIDXYXLRnjtLDNHMIhAZZLSvwfILG6JWczzWbBcgUFH37cu8IaQs1MtpJhFs1rFNlKON7ILCsR9S1pB7nusabshInwfOwYgmVrwHcRO7K7ATVgSSvazG1a0WZi+Vd32dUNmftRDHjrLW2YgATYGtpi8FwDynXBprLgZ1/iwt7X8M7Ud8iKOtOvcIFldHzI4zsQt++XcE43ssxK8vVwGaRqzk60VRB+7P1wBqmtIoNoqyCUN/BhorRWbImWtZmLtJzN5OoGzz52/jN5TI9cYdHL+rLd1QcNEmAraAuq9Ssf1J8z+qpmwVk8/dtH2DmgCfU6FqMH0c2HMXZndY42spaVFM5I88mfIz5vHRmds+uAc7S0zVyi5Wy2hmR4T/nVDSKj9je2PksCTBDES4n41eq2otUNGhmkZ0/Cr/+1GlETJMAEQZQhi40757Gz8ximVw1GVqL8sBedR6ZbpwbfZJAAEwRBuAQJMEEQhEuQABMEQbhEXQJ88uRJetGLXvRqmVezQhkwQRCES5AAEwRBuAQJMEEQhEuQABMEQbgECTBBEIRLkAATBEG4BAkwQRCES5AAEwRBuAQJMEEQhCsA/w/TzIEU0HQPdwAAAABJRU5ErkJggg=="
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![1530329853%281%29.png](attachment:1530329853%281%29.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Python支持的增强型赋值，图片来源：Mark Lutz, Learning Python 5th Edition, p.p.350"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**备注1：Python支持的增强型赋值有的可以应用于对象，比如+=和*=可以应用于列表或字符串。**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**备注2：Python不支持传统的++、--等歧义性较大的增强型赋值，统一写成诸如a += 1的形式。**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "love letter: love you! love letter: love you! love letter: love you! love letter: love you! \n"
     ]
    }
   ],
   "source": [
    "s = 'love letter: love you! '\n",
    "s *= 4\n",
    "print(s)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 赋值语句注意事项"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在使用赋值语句时，建议避免对于不返回值的方法或函数进行赋值，因为它们导致被赋值变量被直接重写为None。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "None\n"
     ]
    }
   ],
   "source": [
    "lst = [1, 2, 3]\n",
    "lst = lst.append(5)\n",
    "print(lst)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 分支语句"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Python 的控制语句格式非常简单，也很常用，为if-elif-else。如果某个分支不需要执行，可以在该分支下插入关键词None或者pass。此外，由于在Python中函数本身也是变量或对象（这点很接近函数式编程的思想），因此每个语句块用于条件判断的表达式可以是对于一个函数的调用。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "input any number:8\n",
      "coco!\n"
     ]
    }
   ],
   "source": [
    "def func1():\n",
    "    return 1 + 1 == 2\n",
    "\n",
    "def func2(num):\n",
    "    return num if num > 10 else ''\n",
    "\n",
    "number = eval(input(\"input any number:\"))\n",
    "\n",
    "# note here if the relationship is or function 2 may not be executed!!!!\n",
    "print('bingo!'if func1() and func2(number) else 'coco!')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "需要注意的是，Python中没有switch语句，一般是用字典（或列表）的方式实现类似其他语言switch语句的功能。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Input ur choice:a\n",
      "default situation is here...\n",
      "Input ur choice:apple\n",
      "苹果机\n",
      "Input ur choice:b\n",
      "boyfriend\n",
      "Input ur choice:stop\n",
      "break\n"
     ]
    }
   ],
   "source": [
    "case = ''\n",
    "\n",
    "result = {'apple':'苹果机',\n",
    "         'b': \"boyfriend\",\n",
    "         'c': 'chair',\n",
    "         'stop' : 'break'}\n",
    "\n",
    "while case != 'stop':\n",
    "    case = input('Input ur choice:')\n",
    "    print(result.get(case, 'default situation is here...'))    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "特别地，使用这种分支还能处理分支中多个状态且每个状态需要执行大块不同代码的情况，有点设计模式中简单工厂模式的味道。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "hello,\n",
      "bug\n",
      "hello, worl\n",
      "25\n"
     ]
    }
   ],
   "source": [
    "def addstr(str1, str2): return str(str1 + str2)\n",
    "\n",
    "def multiply_str(string, a): return string * a\n",
    "\n",
    "def divide_str(string, a): return string[:len(string) // a]\n",
    "\n",
    "def minus_str(string, a): return string[:len(string) - a] if len(string) - a > 0 else 'bug'\n",
    "\n",
    "str_op_factory = {'addstr' : addstr,\n",
    "          'multiply_str' : multiply_str,\n",
    "          'divide_str' : divide_str,\n",
    "          'minus_str' : minus_str,\n",
    "          'others' : lambda x: x ** 2}\n",
    "\n",
    "print(str_op_factory['divide_str']('hello, world!', 2))\n",
    "print(str_op_factory['minus_str']('hello, world!', 25))\n",
    "print(str_op_factory.get('minus_str', \"default_option\")('hello, world!', 2))\n",
    "print(str_op_factory['others'](5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Python中的“\\”符号确实可以用于换行，然而并不推荐使用。更推荐的方式是对换行的表达式使用括号，这种情况在多分支中很常见。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Input anything:hi\n",
      "yeah!\n",
      "93\n"
     ]
    }
   ],
   "source": [
    "a,b,c,d,e,f = [input(\"Input anything:\")] * 6\n",
    "\n",
    "if (a == b and c== d and\n",
    "   e == f):\n",
    "    print('yeah!')\n",
    "\n",
    "x = (1 + 3 + 4 + 5 + 8 + 11\n",
    "     + 13 + 15 + 17 +16)\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 循环语句"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Python的循环语句中，while循环是应对普遍情况的循环，而for循环常用于处理序列或可迭代对象。注意Python的循环为控制语句（statments），而非表达式（expression），同时不具有表达式的作用（如不能在检查条件的同时赋值）。这样可以避免“=”以及“==”的使用错误。此外，python解包也可用于for循环语句中。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a ['b', 'c'] d\n",
      "h ['e', 'l', 'l'] o\n",
      "a ('b', 'c') d\n",
      "h ('e', 'l', 'l') o\n"
     ]
    }
   ],
   "source": [
    "for a,*b,c in [('a', 'b', 'c', 'd'), ('h', 'e', 'l', 'l', 'o')]:\n",
    "    print(a, b, c, sep=' ')\n",
    "    \n",
    "# A similar way using all:\n",
    "for all in [('a', 'b', 'c', 'd'), ('h', 'e', 'l', 'l', 'o')]:\n",
    "    a, b, c = all[0], all[1:-1], all[len(all) - 1]\n",
    "    print(a, b, c)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 可迭代变量（loop variables）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "如果在for语句中使用了len和range函数的组合，则速度比使用迭代慢。因此除非需要明确对应到序列对象的下标，一般不推荐使用len和range的组合或while语句。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**注意：在迭代中需要修改元素时，则需要使用for和range以及len函数的结合。**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a a a a a a a a a a a a ['h', 'e', 'l', 'l', 'o', ',', ' ', 'k', 'i', 't', 't', 'y']\n"
     ]
    }
   ],
   "source": [
    "# Wrong sample:\n",
    "L = list('hello, kitty')\n",
    "\n",
    "for char in L:\n",
    "    char = 'a'\n",
    "    print(char, end=' ')\n",
    "\n",
    "print(L)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "出现这种现象的原因是，每次迭代时，char这个变量会被拷贝到一个单独空间，因此迭代变量的修改对于原对象没有影响。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'a']\n"
     ]
    }
   ],
   "source": [
    "# Wrong sample:\n",
    "L = list('hello, kitty')\n",
    "\n",
    "for i in range(len(L)):\n",
    "    L[i] = 'a'\n",
    "\n",
    "print(L)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 循环否分支（loop else）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Python循环语句后的else（循环否分支，loop else）用于运行循环语句正常退出时的情况（未被break），一个典型的用法即用于查找素数。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Input a number: 15\n",
      "15 has factor 5\n",
      "Input a number: 7\n",
      "7 is prime\n",
      "Input a number: stop\n",
      "Trials terminated!\n"
     ]
    }
   ],
   "source": [
    "trial = True\n",
    "\n",
    "while trial:\n",
    "    try:\n",
    "        num = input(\"Input a number: \")        \n",
    "        num = int(num)\n",
    "\n",
    "        x = num // 2 # For some y > 1\n",
    "        while x > 1:\n",
    "            if num % x == 0: # Remainder\n",
    "                print(num, 'has factor', x)\n",
    "                break # Skip else\n",
    "            x -= 1\n",
    "        else: # Normal exit\n",
    "            print(num, 'is prime')\n",
    "    except ValueError:\n",
    "        trial = False\n",
    "\n",
    "else:\n",
    "    print(\"Trials terminated!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "循环否分支还可以用于搜索序列中某个元素的情况。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Input a number: 5\n",
      "element in list, found at:  1\n",
      "Input a number: 6\n",
      "Element not in the list!\n",
      "Input a number: exit\n"
     ]
    }
   ],
   "source": [
    "trial = True\n",
    "\n",
    "while trial:\n",
    "    lst = [1, 3, 5, 7, 9]\n",
    "    count = 1\n",
    "    \n",
    "    try:\n",
    "        key = int(input(\"Input a number: \"))\n",
    "        \n",
    "        # list note empty:\n",
    "        while lst:\n",
    "            if key == lst[0]: \n",
    "                print('element in list, found at: ', count)\n",
    "                break\n",
    "            lst = lst[1:]\n",
    "        else:\n",
    "            print(\"Element not in the list!\")\n",
    "        \n",
    "    except ValueError: trial = False\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Python 操作符"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## in 操作符"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "用来判断特定元素是否存在于特定对象（字符串列表、元组、集合或字典）中，或者循环中取特定条件迭代对象。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lou_jiaozhu = \"a hacker\"\n",
    "print(\"hack\" in lou_jiaozhu)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "x = set('spam')\n",
    "y = {'m', 'a', 'p'}\n",
    "\n",
    "print(x, y)\n",
    "print('a' in x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 2 3\n",
      "4 5 6\n",
      "7 8 9\n",
      "[(1, 2)] 3\n",
      "[(4, 5)] 6\n",
      "[(7, 8)] 9\n"
     ]
    }
   ],
   "source": [
    "for (*a, b) in [(1,2,3), (4,5,6),(7,8,9)]:\n",
    "    print(*a, b)\n",
    "    \n",
    "for (*a, b) in [((1,2),3), ((4,5),6),((7,8),9)]:\n",
    "    print(a, b)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Python中常用in语句查询键是否存在于字典中，或者值是否存在于字典中。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use 'in' operator in dictionaries:\n",
    "man = dict(name='Bob', job='dev', age=40)\n",
    "print('name' in man)\n",
    "print('occupation' in man)\n",
    "print(40 in man)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## is 操作符"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "is操作符常用来确定对象之间的关系，用例详见第一部分笔记“共享内存”部分。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## “\\*”操作符"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "该操作符表示将一个可迭代对象（包括序列、文件等）进行“解包”，即获取一个序列中的所有元素，然后将这些元素作为参数进行传递，产生其他用途。在赋值操作中，如果该符号出现在等号左边，则表示由程序运行时决定该变量对应的元素数量。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "h ['e', 'l', 'l', 'o', ',', 'w', 'o', 'r', 'l', 'd'] !\n",
      "['h', 'e', 'l', 'l', 'o', ',', 'w', 'o', 'r', 'l'] d !\n",
      "h e ['l', 'l', 'o', ',', 'w', 'o', 'r', 'l', 'd', '!']\n",
      "[1, 2, 3, 4, 5, 6, 7, 8]\n"
     ]
    }
   ],
   "source": [
    "seq = list(\"hello,world!\")\n",
    "a, *b, c = seq\n",
    "print(a,b,c)\n",
    "*a, b, c = seq\n",
    "print(a,b,c)\n",
    "a, b, *c = seq\n",
    "print(a,b,c)\n",
    "\n",
    "a, *b, c = range(10)\n",
    "print(b)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "然而，在赋值操作时，由“ * ”赋值的变量类型为**列表！**。它在某些情况下可能是单元素列表或空列表。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'list'>\n"
     ]
    }
   ],
   "source": [
    "nums = [1,3,5,7]\n",
    "a,b,c,*d = nums\n",
    "print(type(d))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[]\n"
     ]
    }
   ],
   "source": [
    "nums = [1,3,5,7]\n",
    "a,*b,c,d,e = nums\n",
    "print(b)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "如果赋值语句中只有一个带星号变量，则该变量后面要加上逗号。（其实可以用不同形式语句替代）。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 3, 5, 7]\n"
     ]
    }
   ],
   "source": [
    "nums = [1,3,5,7]\n",
    "# Must add a comma in this case.\n",
    "*lst, = nums\n",
    "print(lst)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "对字典进行解包则获得其所有的键。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a b c d\n"
     ]
    }
   ],
   "source": [
    "demo_dict = {'a' : 'argentina', 'b' : 'brazil', 'c':'c ronaldo', 'd':'dutch'}\n",
    "print(*demo_dict)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### \"\\*\"符号在Python 函数中的应用"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "与在赋值操作中获得列表不同，\"\\*\"符号在函数头中表示将传入的元素打包成一个元组进行处理。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 3, 'hi')\n",
      "<class 'tuple'>\n"
     ]
    }
   ],
   "source": [
    "def func(*args):\n",
    "    print(args)\n",
    "    print(type(args))\n",
    "    \n",
    "func(1, 3, 'hi')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## \"\\*\\*\" 操作符"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "无论在方法头中还是在赋值中，该符号均表示获得一个可供处理的python字典。出现在方法头中则表示将调用程序提供的关键字处理成字典传入。在赋值中则表示将字典中的数据按键值对的方式提取并处理。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "My laptop   runs    win32\n",
      "My...Super Server        ...runs...     win32 \n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "\n",
    "data = dict(platform=sys.platform, device='laptop')\n",
    "print(\"My {device:<8} runs {platform:>8}\".format(**data))\n",
    "\n",
    "#Left adjust the tool info and right adjust the platfrom information to 10 positions.\n",
    "template = \"My...{cp[tool]:<20}...runs...{0.platform:>10} \"\n",
    "print(template.format(sys, 'jerk', 'spam', cp=dict(tool='Super Server')))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'song': 'see u again', 'mv': 'fast and furious', 'dude': 'Paul'}\n",
      "<class 'dict'>\n"
     ]
    }
   ],
   "source": [
    "def print_whatever(**kargs):\n",
    "    print(kargs)\n",
    "    print(type(kargs))\n",
    "\n",
    "\"\"\"\n",
    "Note here song cannot be 'song', \n",
    "because this is keyword,\n",
    "rather than expression.\n",
    "\"\"\"\n",
    "print_whatever(song='see u again', mv='fast and furious', dude='Paul')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**注意：在Python函数的参数列表中，\\*操作符的出现需在变量之后，\\*\\*之前。**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Held in:  Russia\n",
      "Teams are:  ('Argitina', 'Brazil', '...')\n",
      "Results:  {'champion': 'France', 'second': 'Croatia', 'third': 'Belgium'}\n"
     ]
    }
   ],
   "source": [
    "def world_cup_2018(place, *teams, **rets):\n",
    "    print(\"Held in: \", place)\n",
    "    print(\"Teams are: \", teams)\n",
    "    print(\"Results: \", rets)\n",
    "\n",
    "world_cup_2018('Russia', 'Argitina', 'Brazil', '...',\n",
    "               champion='France', second='Croatia', third='Belgium')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Python方法和函数"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Python一个强大之处就在于同时支持面向过程和面向对象的编程，从而通过编写函数或从属于类的方法实现代码复用。\n",
    "\n",
    "本笔记将属于命名空间的单个独立模块为函数（function），而从属于类属性的单个模块为方法（method）。"
   ]
  },
  {
   "attachments": {
    "1531356589%281%29.png": {
     "image/png": 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"
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![1531356589%281%29.png](attachment:1531356589%281%29.png)\n",
    "![1531356631%281%29.png](attachment:1531356631%281%29.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Python 函数相关指令或关键字，图片来源：Mark Lutz, Learning Python 5th Edition, p.p.473 - 474"
   ]
  },
  {
   "attachments": {
    "1531709272%281%29.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![1531709272%281%29.png](attachment:1531709272%281%29.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Python 函数参数传入以及处理方式，图片来源：Mark Lutz, Learning Python 5th Edition, p.p.530"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "注意：\n",
    "\n",
    "- def 函数实质上定义了一个对象，名且将对象名赋予了该对象；而lamda通常定义并直接返回一个对象，它往往适用于较短的内部函数。\n",
    "- return 将函数的输出返回给调用方（caller），而yield也有类似作用，但是同时保存了被返回变量的当前状态。\n",
    "- global函数定义了模块级的变量，而nonlocal函数定义了函数级别的变量（即该变量可以由函数外被赋值）\n",
    "- Python中函数调用的过程对于可修改的对象执行地址传递（即函数中的修改会修改调用方对象的属性）。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## python 函数的变量传递规则"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Python对于参数的传递实际上是地址传递，它不产生对变量额外的备份。因此具有以下特点：\n",
    "- 在函数内部对于变量的赋值不影响调用该函数的程序\n",
    "- 在函数内修改可修改变量会对调用方对象产生影响"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100\n"
     ]
    }
   ],
   "source": [
    "var = 100\n",
    "\n",
    "def change_var(var):\n",
    "    var *= 10\n",
    "\n",
    "change_var(var)\n",
    "print(var)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 3, 5, 7, 9, 100]\n"
     ]
    }
   ],
   "source": [
    "lst = [1,3,5,7,9]\n",
    "\n",
    "def append_lst(lst):\n",
    "    lst.append(100)\n",
    "\n",
    "append_lst(lst)\n",
    "print(lst)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "如果不希望可修改对象被修改，则需要进行显式备份。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 3, 5, 7, 9]\n"
     ]
    }
   ],
   "source": [
    "lst = [1,3,5,7,9]\n",
    "\n",
    "def append_lst(lst):\n",
    "    lst_cpy = lst[:]\n",
    "    lst_cpy.append(100)\n",
    "\n",
    "append_lst(lst)\n",
    "print(lst)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## python 函数变量调用顺序"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Python 调用函数时，参数列表必须遵循如下顺序：值、var_name=value的若干关键词键值对、\\*iterable可迭代对象以及\\*\\*dict字典序列。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Python 函数头的参数顺序应当遵循如下规则：值、默认值键值对var_name=value、\\*name类型变量、关键字键值对name=value以及\\*\\*dict字典序列。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Patient name:  Kitty\n",
      "Patient disease:  stroke\n",
      "Patient age:  14\n",
      "Treated:  False\n",
      "\n",
      "Patient name:  Vincent\n",
      "Patient disease:  stunned\n",
      "Patient age:  24\n",
      "Treated:  panicillin\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# 一个关于关键字键值对的传参示例：\n",
    "# An example about passing values, treated as a default value:\n",
    "def hospital_ward(name, age, disease, treated=False):\n",
    "    print(\"Patient name: \", name)\n",
    "    print(\"Patient disease: \", disease)\n",
    "    print(\"Patient age: \", age)\n",
    "    print(\"Treated: \", treated)\n",
    "    print()\n",
    "    \n",
    "hospital_ward(disease='stroke', name='Kitty', age=14)\n",
    "hospital_ward('Vincent', disease='stunned', age=24, treated='panicillin')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**注意：可修改变量用于函数头参数列表作为默认值时，该值在调用过程中会不断变化，而不是每次调用时自动回到初始值。如果需要自动回到初始值，需要在函数体中进行声明。***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Kitty']\n",
      "['Kitty', 'Vincent']\n"
     ]
    }
   ],
   "source": [
    "def patients(name, namelist=[]):\n",
    "    namelist.append(name)\n",
    "    return namelist\n",
    "\n",
    "lst = patients('Kitty')\n",
    "print(lst)\n",
    "\n",
    "lst = patients('Vincent')\n",
    "print(lst)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Kitty']\n",
      "['Kitty', 'Vincent']\n",
      "['Jon']\n"
     ]
    }
   ],
   "source": [
    "def patients(name, namelist=[]):\n",
    "    if len(namelist) >= 2:\n",
    "        namelist = []\n",
    "    \n",
    "    namelist.append(name)\n",
    "    return namelist\n",
    "\n",
    "lst = patients('Kitty')\n",
    "print(lst)\n",
    "\n",
    "lst = patients('Vincent')\n",
    "print(lst)\n",
    "\n",
    "lst = patients('Jon')\n",
    "print(lst)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## print函数"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "print([thing, ..., , [sepa=' ', [end='\\n', [file=sys.stdout, [flush=False]]]]])函数是Python 3中常用来输出的函数。。其中，thing是需要输出的对象，sepa为对象元素之间的间隔符号，默认为空格；end为该输出结束后的符号，默认为换行；file指定输出媒介，默认为屏幕（sys.out），而flush指定输出结束后是否清理输出。\n",
    "\n",
    "flush这个参数在一般客户端编程中不常使用，因此不额外展开探究。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "monte ['P', 'y', 't', 'h', 'o', 'n'] [1, 2, 3]\n"
     ]
    }
   ],
   "source": [
    "x =  'monte'\n",
    "y = list('Python')\n",
    "z = [1, 2, 3]\n",
    "print(x, y, z)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "x =  'monte'\n",
    "y = list('Python')\n",
    "z = [1, 2, 3]\n",
    "print(x, y, z, file=open(\"resource_files/print_out_data.txt\",'w'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Python 标准输出流sys.stdout"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "back to screen...\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "\n",
    "screen_output = sys.stdout #1\n",
    "screen_error = sys.stderr #2\n",
    "\n",
    "sys.stdout = open('resource_files/print_stdout_data.txt', 'w')\n",
    "sys.stderr = open('resource_files/print_stderrlog_data.txt', 'w')\n",
    "x =  'monte'\n",
    "y = list('Python')\n",
    "z = [1, 2, 3]\n",
    "\n",
    "print(x)\n",
    "print(y)\n",
    "print(z)\n",
    "\n",
    "sys.stderr.write(('Oops...') * 4 + \"\\n\")\n",
    "sys.stdout.close()\n",
    "sys.stderr.close()\n",
    "\n",
    "sys.stdout = screen_output #3\n",
    "sys.stderr = screen_error #4\n",
    "\n",
    "print('back to screen...')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1、2、3以及4这四行仅仅作确保流可以切换回屏幕的用途（在Jupyter notebook中是唯一方法），其他平台或IDE中可以使用如下方法将输出流切回屏幕（默认值）。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "sys.stdout = screen_output\n",
    "sys.stderr = screen_error"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## eval函数"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "eval通常用于转化并自动决定被输入数字的数据类型。因此，在转化用户输入的到数值的情况中，它比int或者float等函数有时更推荐使用。（见第4章一个例子）:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Input a nmber please! Number: 2\n",
      "4\n",
      "Input a nmber please! Number: 3.8\n",
      "14.44\n",
      "Input a nmber please! Number: stop\n",
      "STOP\n"
     ]
    }
   ],
   "source": [
    "while True:\n",
    "    msg = input(\"Input a nmber please! Number: \")\n",
    "    \n",
    "    try:\n",
    "        if msg == 'stop' or msg == '停止':\n",
    "            print(msg.upper())\n",
    "            break\n",
    "        digits = eval(msg)\n",
    "    except:\n",
    "        print('Not number! Try again!')\n",
    "    else:\n",
    "        print(digits ** 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "eval('print(\"0o100\")')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 迭代用途函数"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### range函数"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "range函数用于生成一系列整数的迭代对象。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-10, -9, -8, -7, -6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4]\n",
      "[]\n",
      "[10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0, -1, -2]\n",
      "[10, 12, 14, 16, 18]\n"
     ]
    }
   ],
   "source": [
    "print(list(range(-10, 5)))\n",
    "print(list(range(10, -6)))\n",
    "print(list(range(10, -3, -1)))\n",
    "print(list(range(10, 20, 2)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### zip函数"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "zip函数用于拼接两个以上的等长或不等长序列，并在迭代时对每个序列的每个元素做并行处理。序列不等长时，处理的数量以最短的序列为准。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('a', 1, 5.5), ('b', 2, 6.6), ('c', 3, 7.7)]\n",
      "a 1 5.5\n",
      "b 2 6.6\n",
      "c 3 7.7\n"
     ]
    }
   ],
   "source": [
    "x = ['a', 'b', 'c', 'd']\n",
    "y = [1, 2, 3]\n",
    "z = [5.5, 6.6, 7.7]\n",
    "\n",
    "print(list(zip(x, y, z)))\n",
    "\n",
    "for a,b,c in zip(x, y, z):\n",
    "    print(a, b, c)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 两（多）列列表转字典"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "zip函数一个很明显的用途即为将两列或多列列表转化为字典。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'a': ('apple', 1), 'b': ('banana', 3), 'c': ('cat', 5), 'd': ('dog', 7)}\n",
      "{'a': ('apple', 1), 'b': ('banana', 3), 'c': ('cat', 5), 'd': ('dog', 7)}\n",
      "{'a': ('apple', 1), 'b': ('banana', 3), 'c': ('cat', 5), 'd': ('dog', 7)}\n"
     ]
    }
   ],
   "source": [
    "lst_keys = ['a', 'b', 'c', 'd']\n",
    "lst_vals = ['apple', 'banana', 'cat', 'dog']\n",
    "lst_nums = [1, 3, 5, 7, 9]\n",
    "\n",
    "# An easy way:\n",
    "voc_dict = dict(zip(lst_keys, zip(lst_vals, lst_nums)))\n",
    "print(voc_dict)\n",
    "\n",
    "# More complex ways:\n",
    "voc_dict = {k:v for (k,v) in zip(lst_keys, zip(lst_vals, lst_nums))}\n",
    "print(voc_dict)\n",
    "\n",
    "voc_dict = {}\n",
    "for k, v in zip(lst_keys, zip(lst_vals, lst_nums)):\n",
    "    voc_dict[k] = v\n",
    "print(voc_dict)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### enumerate函数"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "enumerate函数对于序列的每个对象，返回一个元组，包含下标及其对应的元素。它的本质是一个迭代器（generator）。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(0, '射')\n",
      "(1, '鸟')\n",
      "(2, '英')\n",
      "(3, '雄')\n",
      "(4, '传')\n"
     ]
    }
   ],
   "source": [
    "lst = list('射鸟英雄传')\n",
    "\n",
    "for ele in enumerate(lst):\n",
    "    print(ele)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(0, 1)\n",
      "(1, 3)\n",
      "(2, 5)\n",
      "(3, 7)\n",
      "迭代完了！\n"
     ]
    }
   ],
   "source": [
    "lst = [1, 3, 5, 7]\n",
    "enum = enumerate(lst)\n",
    "\n",
    "try:\n",
    "    print(next(enum))\n",
    "    print(next(enum))\n",
    "    print(next(enum))\n",
    "    print(next(enum))\n",
    "    print(next(enum))\n",
    "except StopIteration:\n",
    "    print(\"迭代完了！\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### \\_\\_next\\_\\_函数"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\\_\\_next\\_\\_函数用于获取可迭代对象的下一个元素，比如在文件处理中，它的功能类似于readline()方法。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "原标题：俄罗斯世界杯的经济学：盈利将创纪录 美中墨球迷最大方\n",
      "\n",
      "参考消息网7月7日报道 俄罗斯军事观察网7月5日报道称，俄罗斯世界杯已被评为史上最为盈利的一届世界杯。利润不仅来自各支球队参加比赛的直接支出，还得益于电视转播、广告出售和球迷在俄花销等。\n",
      "\n",
      "据美国《纽约时报》报道，国际足联在2010年南非世界杯上赚了39亿美元，在2014年巴西世界杯上赚了48亿美元。\n",
      "\n"
     ]
    }
   ],
   "source": [
    "f = open('resource_files/russian_world_cup.txt')\n",
    "print(f.__next__())\n",
    "print(next(f))\n",
    "print(next(f))\n",
    "f.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**注意：有的对象类内定义了\\_\\_next\\_\\_函数，则不支持使用next()函数对其调用。**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 驱动器 F 中的卷是 VM\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'\\n# This:\\nnext(dir_call)\\n#would be wrong in this case.\\n'"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "\n",
    "dir_call = os.popen('dir')\n",
    "print(dir_call.__next__())\n",
    "\n",
    "\"\"\"\n",
    "# This:\n",
    "next(dir_call)\n",
    "#would be wrong in this case.\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 驱动器 F 中的卷是 VM\n",
      "\n",
      " 卷的序列号是 12F2-6ABC\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# But can use iter function to change it to iterator:\n",
    "import os\n",
    "\n",
    "dir_call = iter(os.popen('dir'))\n",
    "print(dir_call.__next__())\n",
    "print(next(dir_call))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### iter 函数"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "iter函数用于将序列对象转化为可迭代对象。可用于列表以及字典，甚至是range对象以及enumerate对象，常与next函数结合使用。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n",
      "Brazil\n",
      "Germany\n",
      "(0, 'Brazil')\n",
      "(1, 'Germany')\n"
     ]
    }
   ],
   "source": [
    "losts_lst = ['Brazil', 'Germany', 'Japan', 'Korea']\n",
    "\"\"\"\n",
    "# This won't be running, since\n",
    "# it is still not iterable objs yet. \n",
    "print(next(losts_lst))\n",
    "\"\"\"\n",
    "\n",
    "losts_iter = iter(losts_lst)\n",
    "print(losts_iter is losts_iter.__iter__())\n",
    "print(next(losts_iter))\n",
    "print(losts_iter.__next__())\n",
    "\n",
    "# Example of applied on an enumerator:\n",
    "losts_enum = enumerate(losts_lst)\n",
    "losts_iter = iter(losts_enum)\n",
    "print(losts_iter.__next__())\n",
    "print(next(losts_iter))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "由于字典时，可以返回一个键。（类似于对于字典的解包操作）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a\n"
     ]
    }
   ],
   "source": [
    "teams_dict = {'a' : 'Argentina', 'b': 'brazil', 'c' : 'c ronardo', 'd' : 'dutch'}\n",
    "teams_iter = iter(teams_dict)\n",
    "print(next(teams_iter))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### filter 函数"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "filter函数用于过滤（序列中）符合特定条件的元素，并返回其迭代。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['a', 'hi', 1, 2, 3]\n"
     ]
    }
   ],
   "source": [
    "print(list(filter(bool, ['a', None, '', 'hi', 1, 2, 3])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 迭代函数之间的区别"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "range函数与zip、map以及filter函数不同，它支持对于同一个范围对象的多个迭代，其它函数则不支持。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iterator 1: \n",
      "0\n",
      "1\n",
      "2\n",
      "Iterator 2: \n",
      "0\n",
      "1\n"
     ]
    }
   ],
   "source": [
    "R = range(5)\n",
    "it1 = iter(R)\n",
    "\n",
    "print(\"Iterator 1: \")\n",
    "print(next(it1))\n",
    "print(next(it1))\n",
    "print(next(it1))\n",
    "\n",
    "it2 = iter(R)\n",
    "print(\"Iterator 2: \")\n",
    "print(next(it2))\n",
    "print(it2.__next__())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 4)\n",
      "(2, 5)\n",
      "(3, 6)\n",
      "3 0 2\n"
     ]
    }
   ],
   "source": [
    "z_arr = zip([1,2,3], [4,5,6])\n",
    "it1 = iter(z_arr)\n",
    "print(next(it1))\n",
    "print(it1.__next__())\n",
    "\n",
    "it2 = iter(z_arr)\n",
    "print(it2.__next__())\n",
    "\n",
    "maps = map(abs, [-3, 0, 2])\n",
    "it1 = iter(maps)\n",
    "it2 = iter(maps)\n",
    "print(it1.__next__(), next(it1), next(it2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## map函数"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Python的map函数根据给定的函数计算每个迭代变量，常与列表等结合使用返回一系列值。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(list(map(abs,[-1,-2,0,1,2])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "M = [\n",
    "    [1, 2, 3],\n",
    "    [4, 5, 6],\n",
    "    [7, 8, 9]\n",
    "]\n",
    "\n",
    "sums = list(map(sum, M))\n",
    "print(sums)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[104, 101, 108, 108, 111, 44, 107, 105, 116, 116, 121, 33]\n",
      "[104, 101, 108, 108, 111, 44, 107, 105, 116, 116, 121, 33]\n"
     ]
    }
   ],
   "source": [
    "print(list(map(ord, 'hello,kitty!')))\n",
    "\n",
    "# This is the same as:\n",
    "intarr = []\n",
    "for char in 'hello,kitty!':\n",
    "    intarr.append(ord(char))\n",
    "print(intarr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Python 类（class）编程"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## \\_\\_call\\_\\_函数"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "重写\\_\\_call\\_\\_函数用于直接调用类所代表的变量的情况，往往用于在需要时改变类的属性，相当于重写（override）在Python中的应用，往往除了嵌套nonlocal之外对于状态转换的编程。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "class State_Machine:\n",
    "    def __init__(self, init_state, label):\n",
    "        self.current_state = init_state\n",
    "        self.label = label\n",
    "        print(\"Current state:\", self.current_state, \"label: \", self.label)\n",
    "    \n",
    "    def __call__(self,next_state, label):        \n",
    "        self.current_state = next_state\n",
    "        self.label = label\n",
    "        print(\"Current state:\", self.current_state, \"label: \", self.label)\n",
    "\n",
    "machine = State_Machine(0, \"sleep\")\n",
    "machine(1, \"brunch\")\n",
    "machine(2, \"alive\")\n",
    "machine(3, \"dinner\")\n",
    "machine(4, \"die\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Python系统编程"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 一些常用的系统信息"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# A first Python script\n",
    "import sys                  # Load a library module\n",
    "import os\n",
    "print(sys.version.split())\n",
    "print(sys.platform)\n",
    "# 获得当前项目路径\n",
    "print(os.getcwd())\n",
    "print(2 ** 32)              # Raise 2 to a power\n",
    "x = 'Spam!'\n",
    "print(x * 8)                # String repetition"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "模块制作和导入可以参考：https://my.oschina.net/Samyan/blog/872413 ,其中，可以通过打出sys.path了解系统模块的导入位置。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "import pprint\n",
    "pprint.pprint(sys.path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 加载模块的若干注意项"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "python3中，重载模块需要显式调用imp包中的reload模块"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#只加载一次：\n",
    "a = 999\n",
    "import script1\n",
    "import script1\n",
    "print(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "from imp import reload\n",
    "reload(script1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "本质上来说，Python中的每个模块是一个命名空间（namespace），它包含一系列属性（attributes）。属性是指与特定对象有关的变量名，具体可以包含这个命名空间中的类、方法、函数以及变量。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "dir(script1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**注意：使用exec或from加载模块时，可能会导致程序中的现有信息被改写。**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以使用exec(py_commends)函数与Python I/O结合的方式读取python模块。但是与from一样，这可能会导致变量被隐式重写。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = 999\n",
    "exec(open('script1.py').read())\n",
    "print(\"a is:\", a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "win32\n",
      "4294967296\n",
      "Spam!Spam!Spam!Spam!Spam!Spam!Spam!Spam!\n",
      "dead parrot sketch\n",
      "dead\n"
     ]
    }
   ],
   "source": [
    "a = 999\n",
    "from imp import reload\n",
    "from script1 import *\n",
    "print(a)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## os.popen 函数"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "使用os.popen模块可以对于系统信息进行类似文档读取的操作。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 驱动器 F 中的卷是 VM\n",
      "\n",
      " 卷的序列号是 12F2-6ABC\n",
      "\n",
      " F:\\Git\\Code_devs\\Py_Jupyter_Wo\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "\n",
    "file = os.popen('dir')\n",
    "print(file.readline())\n",
    "print(file.read(50))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 驱动器 F 中的卷是 VM\n",
      " 卷的序列号是 12F2-6ABC\n",
      "\n",
      " F:\\Git\\Code_devs\\Py_Jupyter_Workspace\\Python_Advanced_programming 的目录\n",
      "\n",
      "2018/07/06  10:55    <DIR>          .\n",
      "2018/07/06  10:55    <DIR>          ..\n",
      "2018/06/17  11:24    <DIR>          .ipynb_checkpoints\n",
      "2018/06/16  17:30    <DIR>          img_notes\n",
      "2018/07/06  10:55         1,692,209 Python_learning.ipynb\n",
      "2018/06/16  17:30            35,922 Python_primary_data_types.png\n",
      "2018/06/30  16:54    <DIR>          Py_programs\n",
      "2018/06/16  17:30                83 README.md\n",
      "2018/07/05  21:54    <DIR>          resource_files\n",
      "2018/06/16  17:30               310 script1.py\n",
      "2018/06/16  17:30    <DIR>          __pycache__\n",
      "               4 个文件      1,728,524 字节\n",
      "               7 个目录 218,510,229,504 可用字节\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "\n",
    "for line in os.popen('dir'):\n",
    "    print(line.rstrip())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "\n",
    "print(os.system('systeminfo'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "主机名:           DESKTOP-DL4Q4RQ\n",
      "OS 名称:          Microsoft Windows 10 家庭中文版\n",
      "OS 版本:          10.0.14393 暂缺 Build 14393\n",
      "OS 制造商:        Microsoft Corporation\n",
      "OS 配置:          独立工作站\n",
      "OS 构件类型:      Multiprocessor Free\n",
      "注册的所有人:     暂缺\n",
      "注册的组织:       暂缺\n",
      "产品 ID:          00342-34609-17251-AAOEM\n",
      "初始安装日期:     2017/7/7, 02:04:54\n",
      "系统启动时间:     2018/7/2, 23:15:40\n",
      "系统制造商:       Dell Inc.\n",
      "系统型号:         Vostro 3559\n",
      "系统类型:         x64-based PC\n",
      "处理器:           安装了 1 个处理器。\n",
      "                  [01]: Intel64 Family 6 Model 78 Stepping 3 GenuineIntel ~2401 Mhz\n",
      "BIOS 版本:        Dell Inc. 1.2.1, 2016/8/5\n",
      "Windows 目录:     C:\\Windows\n",
      "系统目录:         C:\\Windows\\system32\n",
      "启动设备:         \\Device\\HarddiskVolume1\n",
      "系统区域设置:     zh-cn;中文(中国)\n",
      "输入法区域设置:   zh-cn;中文(中国)\n",
      "时区:             (UTC+10:00) 堪培拉，墨尔本，悉尼\n",
      "物理内存总量:     8,084 MB\n",
      "可用的物理内存:   3,495 MB\n",
      "虚拟内存: 最大值: 9,364 MB\n",
      "虚拟内存: 可用:   3,789 MB\n",
      "虚拟内存: 使用中: 5,575 MB\n",
      "页面文件位置:     C:\\pagefile.sys\n",
      "域:               WORKGROUP\n",
      "登录服务器:       \\\\DESKTOP-DL4Q4RQ\n",
      "修补程序:         安装了 2 个修补程序。\n",
      "                  [01]: KB3199209\n",
      "                  [02]: KB3197954\n",
      "网卡:             安装了 5 个 NIC。\n",
      "                  [01]: Intel(R) Dual Band Wireless-AC 3160\n",
      "                      连接名:      WLAN\n",
      "                      启用 DHCP:   是\n",
      "                      DHCP 服务器: 100.97.80.1\n",
      "                      IP 地址\n",
      "                        [01]: 100.97.83.210\n",
      "                        [02]: fe80::119b:6d3e:4c37:2da7\n",
      "                  [02]: Realtek PCIe GBE Family Controller\n",
      "                      连接名:      以太网\n",
      "                      状态:        媒体连接已中断\n",
      "                  [03]: Bluetooth Device (Personal Area Network)\n",
      "                      连接名:      蓝牙网络连接\n",
      "                      状态:        媒体连接已中断\n",
      "                  [04]: VirtualBox Host-Only Ethernet Adapter\n",
      "                      连接名:      VirtualBox Host-Only Network\n",
      "                      启用 DHCP:   否\n",
      "                      IP 地址\n",
      "                        [01]: 192.168.56.1\n",
      "                        [02]: fe80::b150:1476:3a41:53ec\n",
      "                  [05]: Cisco AnyConnect Secure Mobility Client Virtual Miniport Adapter for Windows x64\n",
      "                      连接名:      以太网 2\n",
      "                      状态:        没有硬件\n",
      "Hyper-V 要求:     虚拟机监视器模式扩展: 是\n",
      "                  固件中已启用虚拟化: 是\n",
      "                  二级地址转换: 是\n",
      "                  数据执行保护可用: 是\n"
     ]
    }
   ],
   "source": [
    "for line in os.popen('systeminfo'):\n",
    "    print(line.rstrip())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Python网络编程"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "b'<HTML>\\n'\n",
      "b'<HEAD>\\n'\n",
      "b'<META HTTP-EQUIV=\"Refresh\" CONTENT=\"20; URL=http://learning-python.com/books\">\\n'\n",
      "b'\\n'\n",
      "b'<link rel=\"shortcut icon\" type=\"image/x-icon\" href=\"http://www.rmi.net/~lutz/favicon.ico\" />\\n'\n",
      "b'\\n'\n",
      "b'<title>Site Redirection Page: index.html</title>\\n'\n",
      "b'\\n'\n",
      "b'<!-- monitor traffic to redirect pages, so know when safe to delete -->\\n'\n",
      "b'<script>\\n'\n",
      "b\"  (function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r;i[r]=i[r]||function(){\\n\"\n",
      "b'  (i[r].q=i[r].q||[]).push(arguments)},i[r].l=1*new Date();a=s.createElement(o),\\n'\n",
      "b'  m=s.getElementsByTagName(o)[0];a.async=1;a.src=g;m.parentNode.insertBefore(a,m)\\n'\n",
      "b\"  })(window,document,'script','//www.google-analytics.com/analytics.js','ga');\\n\"\n",
      "b'\\n'\n",
      "b\"  ga('create', 'UA-69509665-1', 'auto');\\n\"\n",
      "b\"  ga('send', 'pageview');\\n\"\n",
      "b'</script>\\n'\n",
      "b'</HEAD>\\n'\n",
      "b'\\n'\n",
      "b'<BODY bgcolor=cornsilk>\\n'\n",
      "b'\\n'\n",
      "b'<P>\\n'\n",
      "b'<TABLE>\\n'\n",
      "b'\\n'\n",
      "b'<TR>\\n'\n",
      "b'\\n'\n",
      "b'<TD>\\n'\n",
      "b'<TABLE>\\n'\n",
      "b'<TR>\\n'\n",
      "b'<P>\\n'\n",
      "b'<A HREF=\"http://learning-python.com/books/about-lp.html\">\\n'\n",
      "b'<IMG src=\"ora-lp5e-tiny.jpg\" alt=\"[LP5E]\" hspace=6 vspace=3>\\n'\n",
      "b'</A>\\n'\n",
      "b'</TR>\\n'\n",
      "b'\\n'\n",
      "b'<TR>\\n'\n",
      "b'<P>\\n'\n",
      "b'<A HREF=\"http://learning-python.com/books/about-pp.html\">\\n'\n",
      "b'<IMG src=\"ora-pp4e-tiny.jpg\" alt=\"[PP4E]\" hspace=6 vspace=3>\\n'\n",
      "b'</A>\\n'\n",
      "b'</TR>\\n'\n",
      "b'</TABLE>\\n'\n",
      "b'</TD>\\n'\n",
      "b'\\n'\n",
      "b'\\n'\n",
      "b'<TD>&nbsp;&nbsp;&nbsp;&nbsp;\\n'\n",
      "b'<TD>\\n'\n",
      "b'<H1>This Site Has Moved</H1>\\n'\n",
      "b'\\n'\n",
      "b'<P>\\n'\n",
      "b'The resource you accessed now lives at this address:\\n'\n",
      "b'\\n'\n",
      "b'<PRE>\\n'\n",
      "b'    <B><A HREF=\"http://learning-python.com/books\">http://learning-python.com/books</A></B>\\n'\n",
      "b'</PRE>\\n'\n",
      "b'\\n'\n",
      "b'<P>\\n'\n",
      "b'To jump to this item, either click its new address above, \\n'\n",
      "b'or wait to be redirected automatically in 20 seconds.\\n'\n",
      "b'\\n'\n",
      "b'<P>\\n'\n",
      "b'Please update any links accordingly. \\n'\n",
      "b'Why the move?&mdash;read the <A HREF=\"earthlink-outage.html\">backstory</A>.\\n'\n",
      "b'\\n'\n",
      "b'</P>\\n'\n",
      "b'</TD>\\n'\n",
      "b'</TR>\\n'\n",
      "b'</TABLE>\\n'\n",
      "b'</P>\\n'\n",
      "b'\\n'\n",
      "b'<HR>\\n'\n",
      "b'<I>\\n'\n",
      "b'This page was generated on Oct-30-2015 by \\n'\n",
      "b'<A HREF=\"http://learning-python.com/books/about-pp4e.html\">PP4E</A> book example\\n'\n",
      "b'<A HREF=\"site-forward.py\">site-forward.py</A> using this \\n'\n",
      "b'<A HREF=\"template.html\">template</A>.\\n'\n",
      "b'</I>\\n'\n",
      "b'</P>\\n'\n",
      "b'\\n'\n",
      "b'</BODY>\\n'\n",
      "b'</HTML>'\n"
     ]
    }
   ],
   "source": [
    "from urllib.request import urlopen\n",
    "\n",
    "for line in urlopen('http://home.rmi.net/~lutz'):\n",
    "    print(line)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 参考文献"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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